Artificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBonini Neto, Alfredo-
Autor(es): dc.creatorAlves, Dilson Amancio-
Autor(es): dc.creatorMinussi, Carlos Roberto-
Data de aceite: dc.date.accessioned2025-08-21T22:57:25Z-
Data de disponibilização: dc.date.available2025-08-21T22:57:25Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/en15217939-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249377-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249377-
Descrição: dc.descriptionThis paper presents the ANN (Artificial Neural Networks) approach to obtaining complete P-V curves of electrical power systems subjected to contingency. Two networks were presented: the MLP (multilayer perceptron) and the RBF (radial basis function) networks. The differential of our methodology consisted in the speed of obtaining all the P-V curves of the system. The great advantage of using ANN models is that they can capture the nonlinear characteristics of the studied system to avoid iterative procedures. The applicability and effectiveness of the proposed methodology have been investigated on IEEE test systems (14 buses) and compared with the continuation power flow, which obtains the post-contingency loading margin starting from the base case solution. From the results, the ANN performed well, with a mean squared error (MSE) in training below the specified value. The network was able to estimate 98.4% of the voltage magnitude values within the established range, with residues around 10−4 and a percentage of success between the desired and obtained output of approximately 98%, with better result for the RBF (radial basis function) network compared to MLP (multilayer perceptron).-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (Unesp)-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (Unesp)-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (Unesp)-
Descrição: dc.descriptionSchool of Engineering São Paulo State University (Unesp)-
Idioma: dc.languageen-
Relação: dc.relationEnergies-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectcontingency analysis-
Palavras-chave: dc.subjectcontinuation methods-
Palavras-chave: dc.subjectload flow-
Palavras-chave: dc.subjectmaximum loading point-
Palavras-chave: dc.subjectvoltage collapse-
Palavras-chave: dc.subjectvoltage stability margin-
Título: dc.titleArtificial Neural Networks: Multilayer Perceptron and Radial Basis to Obtain Post-Contingency Loading Margin in Electrical Power Systems-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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